
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelBinarizer
import numpy as np
from PIL import Image
import os


# 图片路径和标签
def load_dataset(path):
    X, y = [], []
    for img in os.listdir(path):
        img_path = os.path.join(path, img)
        label = img.split('.')[0]
        for i in range(300):
            pass
    return X, y


# 加载预训练的VGG16模型
# model = VGG16(weights='imagenet', include_top=False)
# model.layers.pop()
#
# print([model.layers[-1].output])
#
# model_outputs = [model.layers[-1].output]
# model.layers[-1].outbound_nodes = []


# 预处理图片
# def preprocess_image(image_path):
#     img = image.load_img(image_path, target_size=(224, 224))
#     x = image.img_to_array(img)
#     x = np.expand_dims(x, axis=0)
#     x = preprocess_input(x)
#     return x


# 使用预训练模型进行特征提取
# def extract_features(image_path):
#     processed_image = preprocess_image(image_path)
#     features = model.predict(processed_image)
#     return features


# 加载数据集
# X, y = load_dataset('H:\\workspace\\pythons\\py001\\baiduImgs')  # 替换为你的数据集路径



# 转换标签
# lb = LabelBinarizer()
# y = lb.fit_transform(y)


# 划分数据集为训练集和测试集
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 特征提取
# X_train_features = [extract_features(img_path) for img_path in X_train]
# X_test_features = [extract_features(img_path) for img_path in X_test]

# 这里可以添加你的分类器代码，例如：
# 创建KNN分类器实例
# knn = KNeighborsClassifier(n_neighbors=10)
# knn.fit(X, y)
# predictions = knn.predict(X)
#
# print(predictions)

# 评估分类器性能
# accuracy_score = accuracy_score(y_test, predictions)

# 注意：这只是一个示例，实际使用时需要根据你的数据集和需求定制分类器。